权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Argumentative Writing Support System for EFL Learners

英语学习者议论文写作支持系统

基本信息

批准号：
20J13239
负责人：
PUTRA JAN WIRA GOTAMA
金额：
$ 1.34万
依托单位：
Tokyo Institute of Technology
依托单位国家：
日本
项目类别：
Grant-in-Aid for JSPS Fellows
财政年份：
2020
资助国家：
日本
起止时间：
2020-04-24 至 2022-03-31
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-20J13239/
关键词：
Argumentative Structure EFL learners ICNALE-AS2R Corpus Annotation Sentence Reordering Structure Parsing EFL students argumentative structure annotation tool TIARA annotator agreement

项目摘要

My research aims to develop a support tool for analysis and improving argumentative essays written by English-as-foreign-language (EFL) learners. The tool extracts and visualizes the argumentative structure of a given essay, and then suggests an improved version of the essay by reordering sentences. This project was divided into several steps:(a) Dataset Construction. In 2020, I constructed the first corpus of 434 essays written by EFL learners (ICNALE-AS2R), which have been annotated with argumentative structure and sentence reordering. I also developed an in-house annotation software (open-source) TIARA for this step.(b) Structure Parsing Model. Given an input essay, I developed a deep learning system to predict the argumentative structure of the essay. I used the combination of Biaffine Attention and BERT models. I also proposed multi-task and multi-corpora training strategies which enhanced the parsing performance of the base model. The idea was to guide the model on how to reduce the search space and to utilise existing non-EFL datasets via selective sampling.(c) Sentence Reordering Model. Given an essay and its corresponding argumentative structure as input, I proposed a reordering system based on the linguistic theory of coherence. The task was formulated as a traversal problem, containing two steps. The first is a pairwise ordering constraint task between pairs of sentences (tackled using ALBERT model). The second is a traversal (output generation) step using an ad-hoc algorithm. Detailed empirical evaluation showed that my proposed system has a good potential.

本研究旨在开发一个支持工具，用于分析和改进英语作为外语（EFL）学习者的议论文。该工具提取并可视化给定文章的论证结构，然后通过重新排序句子来建议文章的改进版本。该项目分为几个步骤：（a）数据集建设。2020年，我构建了第一个包含434篇EFL学习者作文的语料库（ICNALE-AS 2 R），这些语料库已经用议论文结构和句子重排进行了注释。我还为这一步开发了一个内部注释软件（开源）TIARA。(b)结构解析模型。给定一篇输入的文章，我开发了一个深度学习系统来预测文章的论证结构。我使用了Biaffine Attention和BERT模型的组合。本文还提出了多任务和多语料库的训练策略，提高了基本模型的句法分析性能。这个想法是指导模型如何减少搜索空间，并通过选择性抽样利用现有的非EFL数据集。(c)句子重排模型。以一篇文章及其相应的议论文结构作为输入，我提出了一个基于连贯理论的排序系统。该任务被制定为一个遍历问题，包含两个步骤。第一个任务是句子对之间的成对排序约束任务（使用ALBERT模型处理）。第二个是使用ad-hoc算法的遍历（输出生成）步骤。详细的实证评估表明，我提出的系统有很好的潜力。